Information processing apparatus, information processing method, and computer program product

ABSTRACT

According to an embodiment, an information processing apparatus includes a memory and processing circuitry. The processing circuitry is configured to acquire three-dimensional position information of a detection point, acquire target surface shape information indicating a shape of a target surface, and calculate state information of an object according to a first distance of the detection point from the target surface on the basis of the three-dimensional position information and the target surface shape information.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is based upon and claims the benefit of priority fromJapanese Patent Application No. 2017-000308, filed on Jan. 5, 2017; theentire contents of which are incorporated herein by reference.

FIELD

Embodiments described herein relate generally to an informationprocessing apparatus, an information processing method, and a computerprogram product.

BACKGROUND

A system that generates an obstacle map on the basis of positioninformation of points acquired using a distance sensor such as a lasersensor is known. There have been some cases where such a systemerroneously recognizes a point corresponding to a ground surface as athree-dimensional object, thereby erroneously detecting a region thatcan actually travel as an obstacle. Accordingly, there is disclosed asystem that a point whose distance between the point measured by laserirradiation and an estimated road surface is equal to or less than athreshold value is determined to correspond to a road surface and thenremoved on the basis of the estimated road surface shape.

Removing the point corresponding to the road surface, however,necessitates another sensor to determine an obstacle in a direction inwhich the point has been removed. Therefore, in a case where theaccuracy of another sensor is low, the reliability of obstacle detectionis low accordingly. Conventionally, furthermore, there has been somecases where a point on the road surface is erroneously detected as anobstacle in a case where the accuracy of estimation of the road surfaceshape is low. Conventionally, therefore, the reliability of objectdetection has been insufficient.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a diagram illustrating a mobile object;

FIG. 2 is a block diagram illustrating a configuration of the mobileobject;

FIG. 3 is a schematic diagram illustrating partitioned regioninformation;

FIGS. 4A and 4B are explanatory diagrams of estimated ground surfaceshape information;

FIG. 5 is an explanatory diagram of the estimated, ground surface shapeinformation;

FIG. 6 is an explanatory diagram of extraction of detection points;

FIG. 7 is a schematic diagram of the detection points;

FIG. 8 is a schematic diagram of a polar coordinate space;

FIG. 9 is a schematic diagram illustrating existence probabilities;

FIGS. 10A-10F is a schematic diagram illustrating existenceprobabilities;

FIGS. 11A-1 to 11D-2 are schematic diagrams illustrating existenceprobabilities;

FIG. 12 is a schematic diagram illustrating existence probabilities;

FIG. 13 is a diagram illustrating a relationship between regions in thepolar coordinate space and partitioned regions in an orthogonalcoordinate space, the relationship being illustrated in the orthogonalcoordinate space;

FIG. 14 is schematic diagram illustrating an orthogonal coordinate map;

FIG. 15 is an explanatory diagram of existence probabilities integratedin time series;

FIG. 16 is a schematic diagram illustrating a display screen;

FIG. 17 is a flowchart illustrating an example of a procedure ofinformation processing; and

FIG. 18 is a hardware configuration diagram.

DETAILED DESCRIPTION

According to an embodiment, an information processing apparatus includesa memory and processing circuitry. The processing circuitry isconfigured to acquire three-dimensional position information of adetection point, acquire target surface shape information indicating ashape of a target surface, and calculate state information of an objectaccording to a first distance of the detection point from the targetsurface on the basis of the three-dimensional position information andthe target surface shape information.

An information processing apparatus, an information processing method,and a computer program product will be described in detail below withreference to the accompanying drawings.

FIG. 1 is a diagram illustrating an example of a mobile object 10according to the present embodiment.

The mobile object 10 includes an information processing apparatus 20, anoutput unit 10A, an external sensor 10B, an internal sensor 10C, a powercontrol unit 10G, and a power unit 10H.

The information processing apparatus 20 is, for example, a dedicated orgeneral-purpose computer. In the present embodiment, a description willbe given of a case, as an example, where the information processingapparatus 20 is mounted in the mobile object 10.

The mobile object 10 is a movable object. The mobile object 10 is avehicle, a hand truck, an object capable of flying (a manned airplane,an unmanned airplane (e.g., an unmanned aerial vehicle (UAV), a drone),or a robot, for example. Furthermore, the mobile object 10 is a mobileobject that travels through driving operation by a human, or a mobileobject capable of automatically traveling (autonomously traveling)without driving operation by a human, for example. In the presentembodiment, a description will be given of a case, as an example, wherethe mobile object 10 is a vehicle. The vehicle is a two-wheeledautomobile, a three-wheeled automobile, or a four-wheeled automobile,for example. In the present embodiment, a description will be given of acase, as an example, where the vehicle is a four-wheeled automobilecapable of autonomously traveling.

Note that the information processing apparatus 20 is not limited to theconfiguration in which the information processing apparatus 20 ismounted in the mobile object 10. The information processing apparatus 20may be mounted on a stationary object. The stationary object is anobject that is fixed to the ground surface. The stationary object is anunlovable object or an object that is stationary relative to the groundsurface. The stationary object is a guardrail, a pole, a parked vehicle,or a road sign, for example. Furthermore, the information processingapparatus 20 may be mounted on a cloud server that executes processingon the cloud.

The power unit 10H is a driving device mounted in the mobile object 10.The power unit 10H is an engine, a motor, or a wheel, for example.

The power control unit 10G controls the power unit 10H. The power unit10H is driven through the control of the power control unit 10G. Toautomatically drive the mobile object 10, the power control unit 10Gcontrols the power unit 10H on the basis of information that can beacquired from the external sensor 10B and the internal sensor 10C, andexistence probability information derived from processing describedlater, for example. Through the control of the power unit 10H, theaccelerator amount, the brake amount, the steering angle, and the likeof the mobile object 10 are controlled. For example, the power controlunit 10G controls the vehicle so as to keep traveling in the currentlane while avoiding an object such as an obstacle, and keep apredetermined distance or more from a vehicle traveling ahead.

The output unit 10A outputs various kinds of information. In the presentembodiment, the output unit 10A outputs the existence probabilityinformation derived by the information processing apparatus 20. Notethat the existence probability information may be a binary valueindicating existence or non-existence. The details of the existenceprobability information will be described later.

The output unit 10A includes, for example, a communication function thattransmits the existence probability information, a display function thatdisplays the existence probability information, and a sound outputfunction that outputs a sound indicating the existence probabilityinformation. For example, the output unit 10A includes at least one of acommunication unit 10D, a display 10E, and a speaker 10F. Note that theoutput unit 10A including the communication unit 10D, the display 10E,and the speaker 10F will be described as an example in the presentembodiment.

The communication unit 10D transmits the existence probabilityinformation to another apparatus. For example, the communication unit10D transmits the existence probability information to another apparatusvia a publicly-known communication line. The display 10E displays theexistence probability information. The display 10E is a publicly-knownliquid crystal display (LCD), a projection apparatus, or a light, forexample. The speaker 10F outputs a sound indicating the existenceprobability information.

The external sensor 10B is a sensor that recognizes the external worldsurrounding the mobile object 10. The external sensor 10B may be mountedin the mobile object 10 or may be mounted outside of the mobile object10. The outside of the mobile object 10 indicates another mobile objector an external apparatus, for example.

The surroundings of the mobile object 10 are a region within apredetermined range from the mobile object 10. This range is anobservable range of the external sensor 10B. This range may be set inadvance.

The external sensor 10B acquires observation information of the externalworld. The observation information is information indicating a result ofobservation of the surroundings of the position in which the externalsensor 10B is mounted. In the present embodiment, the observationinformation is information from which three-dimensional positioninformation of each of a plurality of detection points in thesurroundings of the external sensor 10B (i.e., the mobile object 10) canbe derived.

The three-dimensional position information of a detection point isinformation indicating a three-dimensional position of the detectionpoint in a real space. For example, the three-dimensional positioninformation of the detection point is information indicating a distancefrom the external sensor 10B to the detection point and a direction ofthe detection point relative to the external sensor 10B as a reference.These distance and direction can be represented by positionalcoordinates indicating the relative position of the detection point withrespect to the external sensor 10B as a reference, positionalcoordinates indicating the absolute position of the detection point, orvectors, for example. Specifically, the three-dimensional positioninformation is represented by polar coordinates or orthogonalcoordinates.

Each detection point indicates an individual point which is individuallyobserved by the external sensor 10B in the external world of the mobileobject 10. For example, the external sensor 10B irradiates thesurroundings of the external sensor 10B with light and receivesreflected light that is reflected off a reflection point. Thisreflection point corresponds to a detection point. Note that a pluralityof reflection points say be used as one detection point.

The external sensor 10B acquires the observation information including alight irradiation direction for each of the plurality of detectionpoints (the directions of the detection points relative to the externalsensor 10B as a reference) and information on the reflected light thatis reflected off each of the plurality of detection points. Theinformation on the reflected light includes the elapsed time from thelight irradiation to the reception of the reflected light and theintensity of the received light (or the attenuation ratio of theintensity of the received light to the intensity of the irradiatedlight), for example.

Then, the external sensor 10B derives the three-dimensional positioninformation of the detection points by using this elapsed time and thelike, and outputs the three-dimensional position information to theinformation processing apparatus 20. Note that the informationprocessing apparatus 20 may derive the three-dimensional positioninformation of the detection points from the observation information. Inthe present embodiment, a description will be given of a case where theexternal sensor 10B outputs the three-dimensional position informationof each of the detection points to the information processing apparatus20.

The external sensor 10B is a photographing apparatus, a distance sensor(a millimeter wave radar, a laser sensor), or a sonar sensor or anultrasonic sensor that detects an object-using sound waves, for example.The photographing apparatus acquires photographed image data(hereinafter, referred to as a photographed image) by photographing. Thephotographing apparatus is a stereo camera, a position specifyingcamera, or the like. The photographed image is digital image datadefining a pixel value for each pixel, a depth map defining a distancefrom the external sensor 10B for each pixel, or the like. The lasersensor is a two-dimensional laser imaging detection and ranging (LIDAR)sensor or a three-dimensional LIDAR sensor, which is mounted parallel toa horizontal plane, for example.

In the present embodiment, a description will be given of a case, as anexample, where the external sensor 10B is a photographing apparatus.Note that in a case where the external sensor 10B is a monocular camera,the external sensor 10B may acquire the three-dimensional positioninformation of detection points on the basis of changes of thephotographed images corresponding to the movement of the mobile object10 using Structure from Motion or the like. In addition, in a case wherethe external sensor 10B is a stereo camera, the external sensor 10B mayacquire the three-dimensional position information of detection pointsusing parallax information between cameras.

In the present embodiment, a description will be given of a case, as anexample, where the external sensor 10B is mounted such that thephotographing direction is the traveling direction of the mobile object10. In the present embodiment, therefore, a description will be given ofa case where the external sensor 10B acquires the three-dimensionalposition information of each of detection points in the travelingdirection (i.e., the front side) of the mobile object 10.

The internal sensor 10C is a sensor that observes the information of themobile object 10 itself. The internal sensor 10C acquires thethree-dimensional position information of the mobile object 10 as wellas self position and attitude information that indicates the attitude ofthe mobile object 10. The internal sensor 10C is an inertial measurementunit (IMU), a speed sensor, or a global positioning system (GPS), forexample. The IMU acquires triaxial acceleration, triaxial angularvelocity, and the like of the mobile object 10. Note that thethree-dimensional position information of the mobile object 10 isrepresented by world coordinates.

Next, the electrical configuration of the mobile object 10 will bedescribed in detail. FIG. 2 is a block diagram illustrating an exemplaryconfiguration of the mobile object 10.

The mobile object 10 includes the information processing apparatus 20,the output unit 10A, the external sensor 10B, the internal sensor 10C,the power control unit 10G, and the power unit 10H. As described above,the output unit 10A includes the communication unit 10D, the display10E, and the speaker 10F.

The information processing apparatus 20, the output unit 10A, theexternal sensor 10B, the internal sensor 10C, and the power control unit10G are connected via a bus 10I. The power unit 10H is connected to thepower control unit 10G.

The information processing apparatus 20 includes a storage unit 20B anda processing unit 20A. In other words, the output unit 10A, the externalsensor 10B, the internal sensor 10C, and the power control unit 10G, theprocessing unit 20A, and the storage unit 20B are connected via the bus10I.

Note that at least one of the storage unit 20B, the output unit 10A (thecommunication unit 10D, the display 10E, and the speaker 10F), theexternal sensor 10B, the internal sensor 10C, and the power control unit10G may be connected to the processing unit 20A by wire or wirelessly.In addition, at least one of the storage unit 20B, the output unit 10A(the communication unit 10D, the display 10E, and the speaker 10F), theexternal sensor 10B, the internal sensor 10C, and the power control unit10G may be connected to the processing unit 20A via a network.

The storage unit 20B stores various kinds of data. The storage unit 20Bis a random access memory (RAM), a semiconductor memory device such as aflash memory, a hard disk, or an optical disk, for example. Note thatthe storage unit 20B may be a storage apparatus that is provided outsidethe information processing apparatus 20. Furthermore, the storage unit20B may be a storage medium. Specifically, the storage medium may be amedium in which programs or various kinds of information is downloadedvia a local area network (LAN), the Internet, or the like and are storedor temporarily stored. Furthermore, a plurality of storage mediums mayconstitute the storage unit 20B.

In the present embodiment, the storage unit 20B stores partitionedregion information in advance. FIG. 3 is a schematic diagramillustrating an example of partitioned region information 30.

The partitioned region information 30 is information indicating apartitioned region B in the surroundings of the mobile object 10. In thepresent embodiment, the partitioned region information 30 is informationindicating each of a plurality of partitioned regions B into which thesurroundings of the mobile object 10 are partitioned. The partitionedregions B are, for example, individual regions acquired by partitioninga three-dimensional space S′ (orthogonal coordinate space) surroundingthe mobile object 10 into a plurality of regions in a grid. Morespecifically, the partitioned regions B are individual regions acquiredby partitioning the three-dimensional space S′ surrounding the mobileobject 10 into a plurality of regions along a plane (XZ plane)orthogonal to the height direction (vertical direction). Note that theorthogonal coordinate space is a three-dimensional space represented byan orthogonal coordinate system. In addition, a polar coordinate spacedescribed later is a three-dimensional space represented by a polarcoordinate system.

The partitioned regions B are not limited to any particular shape. Forexample, the partitioned regions B have a rectangular shape. Inaddition, the partitioned regions B are not limited to a square shape.The partitioned regions B may have a rectangular shape, for example.

Note that the partitioned regions B may be equal to or larger thandetection points in size which are acquired by the external sensor 10B.That is, the partitioned regions B may be the same as the detectionpoints in size. In addition, each partitioned region B may have a sizethat can include a plurality of detection points. That is, the size ofeach partitioned region B may be the same as the size corresponding tothe sensor resolution which is the maximum density of detection pointsthat can be acquired by the external sensor 10B, or may be larger thanthe size corresponding to the sensor resolution. In addition, themaximum possible size of each partitioned region B may be appropriatelyadjusted according to the unit of the existence probabilities ofobstacles.

In the present embodiment, a description will be given of a case wherethe partitioned regions B are larger than detection points in size whichare acquired by the external sensor 10B. In the present embodiment,therefore, a description will be given of a case, as an example, where aplurality of detection points is included in one partitioned region B.

The processing unit 20A generates the partitioned region information 30in advance. For example, the processing unit 20A defines thethree-dimensional space S′. This three-dimensional space S′ is athree-dimensional orthogonal coordinate space with the mobile object 10as the origin, the traveling direction of the mobile object 10 as theZ-axis, the height direction as the Y-axis, and the axis orthogonal tothe Z-axis and the Y-axis as the X-axis. Then, the processing unit 20Apartitions this three-dimensional space S′ into a grid along the XZplane, thereby partitioning the surroundings of the mobile object 10into the plurality of partitioned regions B. Then, the processing unit20A stores the partitioned region information 30 in the storage unit 20Bin advance. The partitioned region information 30 indicates the positionand the size of each of the partitioned regions B.

Note that an external apparatus may generate the partitioned regioninformation 30. In this case, the information processing apparatus 20may acquire the partitioned region information 30 from the externalapparatus via the communication unit 10D and store the partitionedregion information 30 in the storage unit 20B. In the presentembodiment, the storage unit 20B stores the partitioned regioninformation 30 in advance.

The description will continue, returning to FIG. 2. The processing unit20A includes a position acquisition unit 20C, a shape acquisition unit20D, a calculation unit 20E, and an output control unit 20F. Theposition acquisition unit 20C, the shape acquisition unit 20D, thecalculation unit 20E, and the output control unit 20F are implemented byone or a plurality of processors, for example. Each of the above unitsmay be implemented by causing a processor such as a central processingunit (CPU) to execute a program, that is, software, for example. Each ofthe above units may be implemented by a processor such as a dedicatedintegrated circuit (IC), that is, hardware. Each of the above units maybe implemented by using software and hardware in combination. In thecase of using the plurality of processors, each processor may implementone of the units, or may implement two or more of the units.

Note that the term “processor” used in the present embodiment andembodiments described later refers to, for example, a CPU, a graphicalprocessing unit (GPU), an application specific integrated circuit(ASIC), or a circuit of a programmable logic device (e.g., a simpleprogrammable logic device (SPLD), a complex programmable logic device(CPLD), and a field programmable gate array (FPGA)).

The processor implements each of the units above by reading andexecuting the program stored in the storage unit 20B. Note that insteadof storing the programs in the storage unit 20B, the program may bedirectly incorporated into the circuit of the processor. In this case,the processor implements each of the units above by reading andexecuting the program incorporated in the circuit.

The position acquisition unit 20C acquires position-related information.The position-related information includes at least three-dimensionalposition information of the detection points. In the present embodiment,the position-related information includes the three-dimensional positioninformation of the detection points, the partitioned region information30, and the self position and attitude information.

The position acquisition unit 20C acquires the three-dimensionalposition information of the detection points from the external sensor10B. That is, the position acquisition unit 20C acquires thethree-dimensional position information of each of the plurality ofdetection points detected by the external sensor 10B. Specifically, theposition acquisition unit 20C acquires the three-dimensional positioninformation for the detection points corresponding to the density of thedetection points detectable by the external sensor 10B, that is, thenumber corresponding to the resolution of the external sensor 10B. Thethree-dimensional position information of the detection points isrepresented by polar coordinates, for example. Note that the externalsensor 10B may acquire three-dimensional information of the detectionpoints represented by orthogonal coordinates.

Note that the position acquisition unit 20C may acquire thethree-dimensional position information of the detection points from theexternal apparatus. In this case, the position acquisition unit 20C mayacquire the three-dimensional position information of the detectionpoints from the external apparatus via the communication unit 10D.

Furthermore, the position acquisition unit 20C acquires the partitionedregion information 30 from the storage unit 20B. Note that the positionacquisition unit 20C may acquire the partitioned region information 30from the external apparatus via the communication unit 10D.

The self position and attitude information is information indicating thethree-dimensional position and attitude of the mobile object 10. Thatis, the self position and attitude information includes thethree-dimensional position information and attitude information of themobile object 10. In the present embodiment, the position acquisitionunit 20C acquires the self position and attitude information from theinternal sensor 10C. Note that the three-dimensional positioninformation of the mobile object 10 is represented by world coordinates,for example.

Then, the position acquisition unit 20C outputs the position-relatedinformation to the shape acquisition unit 20D and the calculation unit20E. The position-related information includes the three-dimensionalposition information of each of the plurality of detection points, thepartitioned region information 30, and the self position and attitudeinformation.

Next, the shape acquisition unit 20D will be described. The shapeacquisition unit 20D acquires target surface shape informationindicating the shape of a target surface.

The target surface is a surface to be detected by the informationprocessing apparatus 20. The target surface is a ground surface, a roadsurface, or a wall surface, for example. In the present embodiment, adescription will be given of a case, as an example, where the targetsurface is the ground surface.

The target surface shape information is information indicating the shapeof the target surface. In the present embodiment, a description will begiven of a case, as an example, where the target surface shapeinformation is ground surface shape information indicating the shape ofthe ground surface. In the present embodiment, therefore, a descriptionwill be given of a case, as an example, where the shape acquisition unit20D acquires the ground surface shape information of the ground surface.

In the present embodiment, the shape acquisition unit 20D estimates theground surface shape information according to the distribution of thedetection points indicated by each of the plurality of three-dimensionalposition information received from the position acquisition unit 20C.Through this estimation, the shape acquisition unit 20D acquires theground surface shape information.

FIGS. 4A, 4B, and 5 are explanatory diagrams of an example of estimationof ground surface shape information 36.

For example, the shape acquisition unit 20D arranges each detectionpoint P in a corresponding position indicated by the three-dimensionalposition information, as illustrated in FIG. 4A. The three-dimensionalposition information indicates each detection point P in thethree-dimensional space S′ indicated by the partitioned regioninformation 30. Note that in a case where the three-dimensional positioninformation of the detection points P is represented by polarcoordinates, the detection points P may be arranged in thethree-dimensional space S′ of the partitioned region information 30following conversion into the orthogonal coordinates.

FIG. 4A illustrates the detection points P arranged in a certainpartitioned region B. For example, the detection points P are arrangedassuming that the position of the origin in the three-dimensional spaceS′ indicated by the partitioned region information 30 is the currentposition of the mobile object 10.

Then, the shape acquisition unit 20D specifies one detection point Phaving the lowest height (the position in the Y-axis direction) as arepresentative detection point P (representative detection point P′) foreach of the plurality of partitioned regions B indicated by thepartitioned region information 30. This is based on the assumption thatthe detection points P corresponding to the ground surface in a spacesurrounding the mobile object 10 exist at the lowest positions in therespective partitioned regions B. As illustrated in FIG. 4B, therefore,one detection point P is specified as a representative detection pointP′ for each of the plurality of partitioned regions B indicated by thepartitioned region information 30.

Next, the shape acquisition unit 20D performs plane approximation forthe representative detection points P′ specified for the respectivepartitioned regions B in the partitioned region information 30. Forplane approximation, a RANdom SAmple Consensus (RANSAC) method is used,for example.

Specifically, the shape acquisition unit 20D randomly selects threerepresentative detection points P′ from the representative detectionpoints P′ corresponding to respective ones of the plurality ofpartitioned regions B. Then, a plane equation indicating an estimatedplane 32 including the selected three representative detection points P′is calculated. The plane equation indicating the estimated plane 32 isrepresented by the following equation (1).aX+bY+cZ+d=0  (1)

In the equation (1), a, b, c, and d are coefficients representing theshape of the estimated plane 32.

Subsequently, any representative detection points P′ whose errors in theY-axis direction relative to the estimated plane 32 are equal to or lessthan a predetermined threshold value Y_(TH) are set as inliers by theshape acquisition unit 20P. Then, the shape acquisition unit 20D countsthe number of representative points P′ set as inliers (hereinafter,referred to as the number of inliers).

FIG. 5 is a schematic diagram illustrating a relationship between theestimated plane 32 and the representative detection points P′ asinliers. In FIG. 5, the representative detection points P′ as inliersamong the representative detection points P′ are indicated by circles.The representative detection points P′ as inliers are representativedetection points P′ whose errors are equal to or less than the thresholdvalue Y_(TH). In addition, the representative detection points P′ asoutliers are indicated by diamond marks in FIG. 5. The representativedetection points P′ as outliers are representative detection points P′whose errors exceed the threshold value Y_(TH).

Then, the shape acquisition unit 20D repeats the series of processingthe number of predetermined times while changing the combination of theselected representative detection points P′. The series of processingincludes the selection of three representative detection points P′ fromthe plurality of partitioned regions B, the calculation of the planeequation of the estimated plane 32, and the count of the number ofinliers. Then, the shape acquisition unit 20D acquires, as the groundsurface shape information 36, the plane equation of the estimated plane32 used for the calculation of the largest number of inliers.

Note that the shape acquisition unit 20D may acquire the ground surfaceshape information 36 using a method other than the plane approximation.The shape acquisition unit 20D may acquire the ground surface shapeinformation 36 using curved surface approximation, or straight-lineapproximation or curved-line approximation in the YZ plane, for example.

The shape acquisition unit 20D outputs the acquired ground surface shapeinformation 36 to the calculation unit 20E.

The description will continue, returning to FIG. 2. Next, thecalculation unit 20E will be described. The calculation unit 20Ecalculates state information of an object.

The object is an object to be detected by the information processingapparatus 20. The object is, for example, an obstacle or the like thatobstructs the traveling of the mobile object 10. Note that the object isnot limited to an obstacle. In the present embodiment, a descriptionwill be given of a case, as an example, where the object whose stateinformation is calculated by the calculation unit 20E is an obstacle.

The state information is information indicating the state of the object.The state of the object is the existence probability of the object, theprobability that the object does not exist, the occupation ratio of theobject, or the probability that the object does not occupy, for example.The state information indicates the existence probability of the object,for example.

In the present embodiment, a description will be given of a case, as anexample, where the state information indicates the existence probabilityof an obstacle. In the present embodiment, therefore, a description willbe given of a case, as an example, where the calculation unit 20Ecalculates the existence probability information indicating theexistence probability of an obstacle. The existence probability isrepresented by a value in a range of 0.0 or more and 1.0 or less.

The calculation unit 20E calculates the existence probability of anobstacle according to a first distance on the basis of thethree-dimensional position information of the corresponding detectionpoint F acquired from the position acquisition unit 20C and the groundsurface shape information 36 acquired from the shape acquisition unit20D.

The first distance is a distance of the detection point P from theground surface. In other words, the first-distance is the height of thedetection point P from the ground surface.

Specifically, the first distance is represented by a difference betweenthe ground surface having the shape indicated by the ground surfaceshape information 36 and the position in the height direction (Y-axisdirection) of the detection point P indicated by the three-dimensionalposition information. Note that the first distance may be represented bythe length of a perpendicular line drawn from the detection point Parranged at the position indicated by the three-dimensional positioninformation down to the ground surface having the shape indicated by theground surface shape information 36.

In the present embodiment, the calculation unit 20E calculates theexistence probability of an obstacle by calculating the first distancefor the corresponding detection point P using the three-dimensionalposition information of the detection point P and the ground surfaceshape information 36.

The calculation unit 20E calculates a lower existence probability as thefirst distance is shorter. In other words, the calculation unit 20Ecalculates a higher existence probability as the first distance islonger. Note that as the first distance is shorter, the position of thedetection point P is closer to the ground surface. In addition, as thefirst distance is longer, the position of the detection point P ishigher to the ground surface.

Note that in a case where the first distance is equal to or less than 0,the calculation unit 20E calculates a predetermined intermediate valueas the existence probability. The intermediate value is an intermediatevalue of “0.5” between 0.0, which is the minimum value of the existenceprobability, and 1.0, which is the maximum value of the existenceprobability. The intermediate value indicates that the state is unknown.

In the present embodiment, the processing unit 20A treats the regionwith the intermediate value of “0.5” as an “unknown region” whose stateis unknown. The unknown state indicates a state that the processing unit20A cannot confirm the existence probability due to shielding of anobject or the like. In the present embodiment, furthermore, theprocessing unit 20A treats the region with the minimum value of “0.0” ofthe existence probability as a “travelable region” where no obstacleexists. In addition, the processing unit 20A treats the region with themaximum value of “1.0” of the existence probability as an “occupiedregion” where an obstacle exists.

Therefore, as the existence probability approaches the maximum value of“1.0” from the intermediate value of “0.5”, this indicates theincreasing possibility of approaching the “occupied region” from the“unknown region”. In addition, as the existence probability approachesthe minimum value of “0.0” from the intermediate value of “0.5”, thisindicates the increasing possibility of approaching the “travelableregion” from the “unknown region”.

Note that the calculation unit 20E may calculate an existenceprobability that a second distance is included in addition to the firstdistance. The second distance indicates a distance between a referenceposition and the position of the detection point P indicated by thethree-dimensional position information. The reference position is thecurrent position of the mobile object 10 in which the informationprocessing apparatus 20 is mounted. That is, the second distance is adistance between the position of the mobile object 10 and the positionof the detection point P. In the following description, the referenceposition may be referred to as the mobile object 10 or the position ofthe mobile object 10.

In the present embodiment, the calculation unit 20E calculates theexistence probability which is lower as the first distance is shorter,and is lower as the second distance is longer.

Specifically, the calculation unit 20E calculates the existenceprobability according to the first distance of a detection point Pclosest to the reference position in a corresponding angular direction(ϕ direction, which hereinafter may be referred to as an angulardirection ϕ) from the reference position (position of the mobile object10). The angular direction ϕ is the direction of ϕ which is an argumentin polar coordinates. That is, the angular direction ϕ indicates anorientation with the current position of the mobile object 10 as theorigin on the plane orthogonal to the vertical direction.

First, the calculation unit 20E identifies a detection point P whichexists in the closest position to the mobile object 10 for each angulardirection ϕ from the mobile object 10. For the detection point P, thecalculation unit 20E then calculates a lower existence probability asthe first distance is shorter.

Specifically, the calculation unit 20E identifies a detection point Pwhich exists in the closest position to the mobile object 10 for eachangular direction ϕ. For the identified detection point P, thecalculation unit 20E then calculates a lower existence probability asthe first distance of the detection point P is shorter. For theidentified detection point P, as the first distance of the detectionpoint P is shorter, the calculation unit 20E calculates a value closerto the intermediate value of “0.5” as the existence probability of anobstacle at the detection point P, for example.

For this detection point P, in the present embodiment, the calculationunit 20E calculates the existence probability which is lower as thefirst distance is shorter, and is lower as the second distance islonger. In other words, for the detection point P which exists in theclosest position to the mobile object 10 for each angular direction ϕ,the calculation unit 20E calculates the existence probability which islower as the height from the ground surface (first distance) is shorter,and is lower as the distance from the mobile object 10 (second distance)is longer.

Then, along a line extending in the angular direction ϕ from theposition of the mobile object 10 in the polar coordinate space, thecalculation unit 20E calculates a model indicating the change in theexistence probability according to the distance from the position of themobile object 10 as well as the existence probability of the detectionpoint P which exists in the closest position to the mobile object 10 inthe corresponding angular direction.

For example, the calculation unit 20E sets the existence probabilitiesof the minimum value in a direction approaching from the mobile object10 to the detection point P closest to the mobile object 10 for eachline along the corresponding angular direction ϕ from the referenceposition with the mobile object 10 as a reference in the polarcoordinate space. Furthermore, the calculation unit 20E sets a modelindicating the existence probabilities along the line extending in thecorresponding angular direction ϕ with the mobile object 10 as areference. In the model, as the distance is closer to the detectionpoint P which is closest to the mobile object 10, the existenceprobabilities change from the minimum value to the existence probabilityof the detection point P. Furthermore, the calculation unit 20Ecalculates the existence probabilities such that a region far from themobile object 10 than the detection point P closest to the mobile object10 is set to the existence probability of the intermediate value.

At this time, it is preferable that for each line extending in thecorresponding angular direction ϕ from the reference position with themobile object 10 as a reference in the polar coordinate space, thecalculation unit 20E calculates a model indicating the existenceprobabilities which smoothly change before and after the detection pointP closest to the mobile object 10 along the corresponding line. Theregions where the existence probabilities change smoothly may be set inadvance. As the regions where the existence probabilities smoothlychange, the calculation unit 20E may use a predetermined number ofpartitioned regions B other than and adjacent to a partitioned region Bincluding the detection point P which exists in the closest position tothe mobile object 10 for each of the angular directions ϕ, for example.

In the present embodiment, the calculation unit 20E calculates theexistence probabilities that satisfy the above conditions by calculatingthe equations.

Hereinafter, a further detailed description will be given of thecalculation of the existence probabilities by the calculation unit 20Ein the present embodiment.

First, the calculation unit 20E acquires the position-relatedinformation from the position acquisition unit 20C. As described above,the position-related information includes the three-dimensional positioninformation of each of the plurality of detection points P, thepartitioned region information 30, and the self position and attitudeinformation.

Subsequently, the calculation unit 20E extracts detection points P to beused for the calculation of the existence probabilities among theplurality of detection points P indicated by each of the plurality ofpieces of three-dimensional position information acquired from theposition acquisition unit 20C.

FIG. 6 is an explanatory diagram illustrating an example of extractionof detection points P. Among the plurality of detection points P, thecalculation unit 20E extracts detection point P located in a spacewithin a predetermined range in a three-dimensional space as detectionpoints P used for calculation of the existence probabilities. In thepredetermined range, the traveling direction Z of the mobile object 10is the central axis L. The central axis L is a long straight line thatpasses through a predetermined position in the mobile object 10 and thatextends in a direction along the traveling direction Z of the mobileobject 10. The predetermined position in the mobile object 10 is thecenter of the body of the mobile object 10, or the center in the heightdirection of a tire provided in the mobile object 10, for example.

For example, assume that the external sensor 10B is mounted in themobile object 10 such that the optical axis of the external sensor 10Bmatches the center of the mobile object 10 and the traveling directionZ. In this case, the optical axis of the external sensor 10B matches thecentral axis L.

Then, the calculation unit 20E uses the space of a predetermined range Ras a space from which detection points P are extracted. Thepredetermined range R includes the central axis L and is centered on thecentral axis L. The space in this predetermined range is a region R thatincludes the central axis L and has a range of ±R′ in the vehicle heightdirection of the mobile object 10 with the central axis L as the center.Note that the range of the region R is adjusted in advance such that theregion does not include a ground surface G while the mobile object 10 istraveling. For example, the length in the vertical direction of theregion R is less than the vehicle height T of the mobile object 10.

In the present embodiment, a description will be given of a case wherethe external sensor 10B is arranged so that the line passing through thecenter of the body of the mobile object 10 matches the optical axis.Then, in the present embodiment, a description will be given of a casewhere the calculation unit 20E uses the optical axis of the externalsensor 10B as the central axis L. Then, among the plurality of detectionpoints P detected by the external sensor 10B, the calculation unit 20Eextracts detection points P located in the predetermined range R asdetection points P used for calculation of the existence probabilities.

This processing excludes, for example, a detection point P indicated bya triangle and a detection point P indicated by a diamond from thecalculation of the existence probabilities as illustrated in FIG. 6.Then, a detection point P indicated by a circle is extracted for thecalculation of the existence probability.

In this way, the calculation unit 20E extracts the detection points Pfor the calculation of the existence probabilities among the pluralityof detection points P included in the angle of view of the externalsensor 10B. As a result, the calculation unit 20E extracts any detectionpoint P that can be an obstacle to the traveling of the mobile object10.

Subsequently, in a case where the three-dimensional position coordinatesof the extracted detection points P are represented by orthogonalcoordinates, the calculation unit 20E converts the three-dimensionalposition coordinates into polar coordinates. For the conversion into thepolar-coordinates, the following equations (2) and (3) are used, forexample. Note that in a case where the three-dimensional positioncoordinates of the detection points P are represented by polarcoordinates, this conversion processing is unnecessary.r=√{square root over (x ² +z ²)}  (2)θ=α tan(z/x)  (3)

Through this processing, the calculation unit 20E obtains a plurality ofdetection points P for the calculation of the existence probabilities,which is represented by the three-dimensional position coordinates ofthe polar coordinates.

FIG. 7 is a schematic diagram of detection points P represented by thethree-dimensional position coordinates of the polar coordinates. FIG. 7illustrates each of the detection points P measured in a scene where themobile object 10 is reaching a T-intersection surrounded by fences.

Next, the calculation unit 20E identifies a detection point P located atthe closest distance r to the mobile object 10 for each angulardirection ϕ indicated by the polar coordinates.

FIG. 8 is a schematic diagram illustrating a polar coordinate space S1surrounding the mobile object 10 for the scene in FIG. 7. The polarcoordinate space S1 is divided into nine in the angular direction ϕevery 20°. For each angular direction ϕ, the calculation unit 20Eidentifies a detection point P at the distance r closest to the mobileobject 10.

Note that the distance r corresponds to the second distance.

Subsequently, the calculation unit 20E calculates, for each angulardirection ϕ, the existence probability of an obstacle for each of aplurality of regions B′ arranged along the respective angular directionsϕ from the mobile object 10 by using the following equations (4) and(5). The regions B′ are regions formed by dividing the polar coordinatespace S1 into a plurality of regions. It is preferable that the regionsB′ are equal to or smaller than the above-described partitioned regionsB in size.

$\begin{matrix}{{p(r)} = {\frac{\alpha}{\sqrt{2\;\pi\;\sigma}}\exp\frac{- \left( {r - r_{0}} \right)^{2}}{2\;\sigma^{2}}}} & (4) \\{{p^{\prime}(r)} = \left\{ \begin{matrix}{\max\left( {p_{\min},{p(r)}} \right)} & {r \leq r_{0}} \\{\max\left( {0.5,{p(r)}} \right)} & {r > r_{0}}\end{matrix} \right.} & (5)\end{matrix}$

In the equations (4) and (5), p(r) indicates a temporary existenceprobability of an obstacle. The temporary existence probabilityindicates a temporary value in the process of calculating the existenceprobability. The distance in the polar coordinates is indicated by r.The distance r from the origin in the polar coordinates to a detectionpoint P closest to the mobile object 10 in the corresponding angulardirection ϕ to be processed is indicated by r₀. The origin in the polarcoordinates is derived from the self position and attitude informationof the mobile object 10.

In the equation (4), σ indicates a standard deviation in the depthdirection of the three-dimensional position information acquired by theposition acquisition unit 20C. σ can be acquired in advance depending onthe characteristics of the external sensor, and the value obtained inadvance is used. In the equation (5), p′(r) indicates the existenceprobability of an obstacle. A predetermined minimum value of theexistence probability is indicated by P_(min). In the presentembodiment, P_(min) is “0.0”.

α is determined according to the first distance and the second distanceof a detection point P which exists in the closest position to themobile object 10 in the corresponding angular direction ϕ to beprocessed. The first distance is the height of the detection point Pfrom the ground surface. The second distance is the distance r₀ of thedetection point P from the mobile object 10. In addition, the equation(4) includes the elements (r and r₀) indicating the distance (seconddistance) of the detection point P.

In other words, in the present embodiment, the calculation unit 20Ederives, for each angular direction ϕ, α according to the first distanceand the second distance of a detection point P which exists in theclosest position to the mobile object 10 in the corresponding angulardirection ϕ. Then, the calculation unit 20E calculates, for each angulardirection ϕ, the existence probability p′ (r) for each distance r fromthe mobile object 10 along each of the lines extending along therespective angular directions ϕ by using the equations (4) and (5)above.

As a result, the calculation unit 20E calculates the existenceprobabilities for each of the angular directions ϕ with the mobileobject 10 as a reference. In other words, the calculation unit 20Ecalculates the existence probability for each of the plurality ofregions B′ through which the line passes along each of the angulardirections ϕ with the mobile object 10 as a reference.

A detailed description will be given of α.

The calculation unit 20E calculates α for each angular direction ϕindicated by the polar coordinates. For example, the calculation unit20E divides the polar coordinate space S1 in front of the travelingdirection of the mobile object 10 in the angular direction ϕ every 20°(nine divisions). Then, the calculation unit 20E sets each angulardirection ϕ as a direction to be processed, and then performs thefollowing processing.

First, the coordinates of a detection point P closest to the mobileobject 10 (i.e., a detection point P at the distance r₀) in thecorresponding angular direction ϕ to be processed is set as (X_(i),Y_(i), Z_(i)) by the calculation unit 20E. Furthermore, the calculationunit 20E acquires the plane equation indicating the ground surface shapeinformation 36 calculated by the shape acquisition unit 20D. Asdescribed above, the plane equation is represented by the above equation(1).

By using the following equation (6), the calculation unit 20Esubsequently calculates the first distance of the detection point Pclosest to the mobile object 10 (the detection point P at the distancer₀) in the corresponding angular direction ϕ to be processed. The firstdistance is the distance from the ground surface.

$\begin{matrix}{h_{1} = {\max\left( {0.0,{Y_{i} - \left( \frac{{- {aX}_{i}} - {cZ}_{i} - d}{b} \right)}} \right)}} & (6)\end{matrix}$

In the equation (6), h_(i) indicates the first distance of the detectionpoint P closest to the mobile object 10 (the detection point P at thedistance r₀). The first distance is the distance from the groundsurface. In the equation (6), a, b, and c are similar to those in theabove equation (1). In the equation (6), furthermore, X_(i), Y_(i),Z_(i) are the coordinates of the detection point P closest to the mobileobject 10 (i.e., the detection point P at the distance r₀).

Using the following equation (7), the calculation unit 20E calculatesthe value of α in the corresponding angular direction ϕ to be processedby using the first distance h_(i) acquired by the equation (6). Thefirst distance h_(i) is the distance, from the ground surface, of thedetection point P (the detection point P at the distance r₀) closest tothe mobile object 10 in the corresponding angular direction.

$\begin{matrix}{\alpha = {\min\left( {{\max\left( {0.5,\frac{h_{i}}{h_{TH}}} \right)},1.0} \right)}} & (7)\end{matrix}$

In the equation (7), h_(TH) indicates a threshold value of the firstdistance of the detection point P from the ground surface.

That is, in a case where h_(i) calculated by the equation (6) is equalto or larger than the threshold value h_(TH) (h_(i)≥h_(TH)), α becomes 1according to the above equation (7). Furthermore, in a case where h_(i)calculated by the equation (6) is less than the threshold value h_(TH)(hi<h_(TH)), α becomes a value larger than 0.5 and less than 1 accordingto the above equation (7). The longer h_(i), the larger α becomes.

Note that h_(TH) may be a predetermined fixed value. Furthermore, thecalculation unit 20E may set the threshold value h_(TH) according to themobile object 10 and the distance r₀ between the mobile object 10 andthe detection point P closest to the mobile object 10 in thecorresponding angular direction ϕ to be processed.

In the present embodiment, the calculation unit 20E calculates thethreshold value h_(TH) using the following equation (8).h _(TH)=tan θ_(th) ×r  (8)

In the equation (8), θ_(th) indicates a threshold value of the angle θof the detection point P with respect to the ZX plane in the polarcoordinate space. In other words, θ_(th) indicates a threshold value ofthe angle θ of the detection point P with respect to the ground surfacewith the mobile object 10 as the origin. In the equation (8),furthermore, r indicates the distance r₀ between the mobile object 10and the detection point P closest to the mobile object 10 in thecorresponding angular direction ϕ to be processed.

The calculation unit 20E calculates the threshold value h_(TH) using theequation (8). In this way, the calculation unit 20E may calculate thethreshold value h_(TH) according to the distance r₀ between the mobileobject 10 and the detection point P closest to the mobile object 10 inthe corresponding angular direction ϕ to be processed. Therefore, thelonger the distance r₀, the larger the threshold value h_(TH) is used bythe calculation unit 20E.

In this way, the calculation unit 20E calculates α for each angulardirection ϕ to be processed by using the equations (6) and (7). αcorresponds to the first distance (the distance from the ground surface)and the second distance (the distance r₀ from the mobile object 10) ofthe detection point P closest to the mobile object 10 in thecorresponding angular direction ϕ.

Then, the calculation unit 20E calculates, for each angular direction ϕto be processed, the existence probability of an obstacle using thecalculated α for each of the regions B′ along the respective angulardirections by using the equations (4) and (5).

Therefore, the calculation unit 20E calculates the existence probabilityof an obstacle according to the conditions of the first distance and thesecond distance for each of the regions B′ along the respective angulardirections ϕ.

FIGS. 9 to 12 are schematic diagrams illustrating the existenceprobabilities calculated by the calculation unit 20E.

FIG. 9 is a schematic diagram illustrating the plurality of extractedregions B′ arranged along the angular direction “60°” in FIG. 8.

As illustrated in FIG. 9, assume that among the plurality of regions B′arranged along this angular direction ϕ (e.g., a region B′1 to a regionB′11), a partitioned region B′8(B′) is a region B′ at the distance r₀that includes a detection point F closest to the mobile object 10. Inthis case, the calculation unit 20E calculates the existenceprobabilities represented by a model 50A by calculating the existenceprobabilities for the line along this angular direction ϕ by using theabove equations (4) to (8).

That is, as illustrated in FIG. 9, the existence probabilities in theregions B′ (B′1 to B′11) sequentially arranged along the angulardirection ϕ are represented by the model 50A which changes smoothlybefore and after the region B′8 including the detection point P closestto the mobile object 10. Furthermore, the existence probability of anobstacle in each region B′ is a value corresponding to the firstdistance (the distance from the ground surface) of the detection pointP, which exists in the closest position to the mobile object 10, and thesecond distance of the detection point P (the distance r₀ from themobile object 10) in the corresponding angular direction ϕ as a resultof calculation using the above equations (4) to (8).

FIG. 10A to FIG. 10F are explanatory diagrams illustrating examples ofthe existence probabilities of obstacles calculated by the calculationunit 20E.

FIG. 10A to FIG. 10F illustrate the examples of the existenceprobabilities of obstacles for a certain angular direction ϕ in thepolar coordinate space S1. In addition, FIG. 10A to FIG. 10F illustratethree different types of forms in which the distance r₀ to a detectionpoint P closest to the mobile object 10 is the same but the height ofthe detection point P from the ground surface (first distance) isdifferent from one another.

FIG. 10B is a model 50B illustrating the existence probabilities ofobstacles in a line along a certain angular direction ϕ for a case wherethe first distance (the height from the ground surface G) of thedetection point P closest to the mobile object 10 is D1 in this angulardirection ϕ (see FIG. 10A). The first distance D1 is an example of theheight which is sufficiently away from the ground surface.

FIG. 10D is a model 50C illustrating the existence probabilities ofobstacles in a line along a certain angular direction ϕ for a case wherethe first distance (the height from the ground surface G) of thedetection point P closest to the mobile object 10 is D2 in this angulardirection ϕ (see FIG. 10C). FIG. 10C is a state where the mobile object10 is traveling uphill, for example. Note that the first distance D2 isan example of the height which is lower than the first distance D1 andhigher than the ground surface. That is, the illustrated relationship isthe first distance D1>the first distance D2>0.

Furthermore, FIG. 10F is a model 50D illustrating the existenceprobabilities of obstacles in a line along a certain angular direction ϕfor a case where the first distance (the height from the ground surfaceG) of the detection point P closest to the mobile object 10 is D3 inthis angular direction ϕ (see FIG. 10E). FIG. 10F is a state where themobile object 10 is traveling downhill, for example. Note that the firstdistance D3 is an example of the height where the position of thedetection point P is on the ground surface. That is, the illustratedrelationship is the first distance D3=0.

As illustrated in FIGS. 10B, 10D and 10F, as the first distance of thedetection point P which exists in the closest position to the mobileobject 10 is shorter, the calculation unit 20E calculates a lowerexistence probability for the detection point P by using the aboveequations (4) to (8). For this detection point P, for example, thecalculation unit 20E calculates the existence probability of “0.8” inthe case of the first distance D1, the existence probability of “0.6” inthe case of the first distance D2, and the existence probability of“0.5” in the case of the first distance D3.

Furthermore, the calculation unit 20E calculates, for each angulardirection the existence probability of “0.0” of the obstacle toward thedetection point P located at the closest distance r₀ from the positionof the mobile object 10 in the corresponding angular direction ϕ byusing the above equations (4) to (8). In approaching the detection pointP at the distance r₀, the calculation unit 20E changes the existenceprobability toward the existence probability of the detection point P atthe distance r₀. Then, the calculation unit 20E calculates theintermediate value of “0.5”, which is an “unknown region”, as theexistence probability for a region B′ at the distance r far from themobile object 10 than the distance r₀.

Therefore, the calculation unit 20E calculates the existenceprobabilities which change smoothly before and after the region B′including the detection point P closest to the mobile object 10. Theseare represented by the models 50B to 50B as the existence probabilitiesfor the regions B′ which sequentially exist in the line along thecorresponding angular direction ϕ.

FIGS. 11A-1 to 11D-2 illustrate examples of the existence probabilitiesof obstacles for a certain angular direction ϕ in the polar coordinatespace S1. In addition, FIGS. 11A-1 to 11D-2 illustrate a plurality offorms in which the distance r₀ (second distance) to a detection point Pclosest to the mobile object 10 is different from one another.

FIG. 11A-2 illustrates models 50 (a model 50E and a model 50F)illustrating two types of existence probabilities calculated using acorresponding one of two types of detection points P (a detection pointP1, a detection point P2) located at different distances r₀ from themobile object 10. As illustrated in FIG. 11A-1, assume a case where thedistance r₀ of the detection point P1 closest to the mobile object 10 isr₀₁ and a case where the distance r₀ of the detection point P closest tothe mobile object 10 is r₀₂, for example. Note that the distancer₀₁<distance r₀₂ is assumed. Furthermore, assume that the firstdistances D which are the heights of these detection points P (thedetection point P1 and the detection point P2) from the ground surfaceare the same.

In a case where the detection point P closest to the mobile object 10 isthe detection point P1, the calculation unit 20E calculates the model50E representing the existence probabilities illustrated in FIG. 11A-2by performing the above processing. In addition, in a case where thedetection point P closest to the mobile object 10 is the detection pointP2, the calculation unit 20E calculates the model 50F representing theexistence probabilities illustrated in FIG. 11A-2 by performing theabove processing.

By using the above equations (4) to (8) in this way, the calculationunit 20E calculates a lower existence probability for the detectionpoint P which exists in the closest position to the mobile object 10 asthe distance r₀ of the detection point P is longer.

In addition, it can also be said that the calculation unit 20E cancalculate the existence probability which corresponds to the angle θ(θ₁,θ₂) of the detection point P to the ground surface with a point ofintersection between a point immediately below the mobile object 10 andthe ground surface as the origin by calculating the existenceprobability using the above equations (4) to (8). The detection point Pis the one which exists in the closest position to the mobile object 10.That is, the larger the angle θ, the higher the calculation unit 20Ecalculates the existence probability for the detection point P.

Furthermore, the calculation unit 20E calculates, for each angulardirection ϕ, the existence probability of “0.0” of the obstacle towardthe detection point P located at the closest distance r₀ from theposition of the mobile object 10 in the corresponding angular directionϕ by using the above equations (4) to (8). In approaching the detectionpoint P at the distance r₀, the calculation unit 20E changes theexistence probability toward the existence probability of the detectionpoint P at the distance r₀. Then, the calculation unit 20E calculatesthe intermediate value of “0.5”, which is an “unknown region”, as theexistence probability for a region B′ at the distance r farther from themobile object 10 than the distance r₀.

Note that while the mobile object 10 is traveling, there are some caseswhere the optical axis L of the external sensor 10B is oriented in adirection intersecting the ground surface. Even in such cases, thecalculation unit 20E can calculate the existence probability accordingto the conditions of the above-described first distance and seconddistance regardless of the orientation of the optical axis of theexternal sensor 10B by calculating the existence probability using theabove equations (4) to (8).

For example, a description will be given with reference to FIG. 11B-2. Amodel 50G in FIG. 11B-2 is a model 50 illustrating the existenceprobabilities in a certain angular direction ϕ for a case where thefirst distance (the height from the ground surface) of a detection pointP3 closest to the mobile object 10 is D4 and the distance r₀ is adistance r₀₁ (see FIG. 11B-1). In addition, a model 50I in FIG. 11B-2 isa model 50 illustrating the existence probabilities in a certain angulardirection ϕ for a case where the first distance (the height from theground surface) of a detection point P4 closest to the mobile object 10is D5 (where D4>D5) and the distance r₀ is a distance r₀₂ (see FIG.11B-1). Note that the illustrated relationship is r₀₁<r₀₂.

Then, the calculation unit 20E calculates the existence probability forthe detection point P by using the above equations (4) to (8). Theexistence probability is lower as the first distance D of the detectionpoint P which exists in the closest position to the mobile object 10 isshorter, and is lower as the second distance r₀ of the detection point Pwhich exists in the closest position to the mobile object 10 is longer.

By calculating the existence probability using the above equations (4)to (8), therefore, the calculation unit 20E can calculate the existenceprobability according to the conditions of the above-described firstdistance and second distance regardless of the orientation of theoptical axis of the external sensor 10B.

Note that there are some cases where the mobile object 10 travels on theground surface inclined with respect to the mobile object 10. Even insuch cases where the ground surface is inclined with respect to themobile object 10, the calculation unit 20E can calculate the existenceprobability according to the conditions of the above-described firstdistance and second distance regardless of the degree of inclination ofthe ground surface by calculating the existence probability using theabove equations (4) to (8).

For example, a description will be given with reference to FIG. 11C-2and FIG. 11D-2.

A model 50J in FIG. 11C-2 is a model 50 illustrating the existenceprobabilities for a case where the first distance (the height from theground surface) of a detection point P5 closest to the mobile object 10is D6 and the distance r₀ is a distance r₀₁ (see FIG. 11C-1). Inaddition, a model 50K in FIG. 11C-2 is a model 50 illustrating theexistence probabilities for a case where the first distance (the heightfrom the ground surface) of a detection point P6 closest to the mobileobject 10 is D7 (where D6>D7, D7=0) and the distance r₀ is a distancer₀₂ (see FIG. 11C-1). Note that the illustrated relationship is r₀₁<r₀₂.

A model 50L in FIG. 11D-2 is a model 50 illustrating the existenceprobabilities for a case where the first distance (the height from theground surface) of a detection point P7 closest to the mobile object 10is D8 and the distance r₀ is a distance r₀₁ (see FIG. 11D-1). Inaddition, a model 50M in FIG. 11D-2 is a model 50 illustrating theexistence probabilities for a case where the first distance (the heightfrom the ground surface) of a detection point P8 closest to the mobileobject 10 is D9 (where D8>D9) and the distance r₀ is a distance r₀₂ (seeFIG. 11D-1). Note that the illustrated relationship is r₀₁<r₀₂.

In this way, the calculation unit 20E calculates, for each angulardirection ϕ, the existence probability for the detection point P whichexists in the closest position to the mobile object 10 by using theabove equations (4) to (8). The existence probability for the detectionpoint P is lower as the first distance (height) of the detection point Pis shorter, and is lower as the second distance (distance r₀) of thedetection point P is longer. In addition, the calculation unit 20E alsocalculates the existence probabilities of obstacles for the regions B′at the distances r other than the distance r₀ by using the aboveequations (4) and (5).

Therefore, even in a case where the ground surface is inclined withrespect to the mobile object 10, the calculation unit 20E can calculatethe existence probability according to the conditions of theabove-described first distance and second distance regardless of thedegree of inclination of the ground surface.

The description will continue, returning to FIG. 2. As described above,the calculation unit 20E calculates the existence probabilities ofobstacles for respective regions B′. That is, the calculation unit 20Ecalculates the existence probability of an obstacle for each of theregions B′ by the above equations, using the distance r of thecorresponding region B′ from the mobile object 10 as well as the height(first distance), from the ground surface, of the closest detectionpoint P in the corresponding angular direction ϕ with the mobile object10 as a reference and the distance r₀ (second distance) of thisdetection point P from the mobile object 10.

FIG. 12 is a schematic diagram illustrating an example of a polarcoordinate map M1. The polar coordinate map M1 represents the existenceprobability of an obstacle calculated by the calculation unit 20E foreach of the regions B′ in the polar coordinate space S1. In FIG. 12, thelateral direction (horizontal direction) indicates the angular directionϕ with the mobile object 10 as the origin in the polar coordinate spaceS1, and the longitudinal direction (vertical direction) indicates thedistance r from the mobile object 10.

FIG. 12 is an example where the polar coordinate space S1 in front ofthe traveling direction of the mobile object 10 is divided in theangular direction ϕ every 20° (nine divisions). The polar coordinate mapM1 illustrated in FIG. 12 can be obtained by the calculation unit 20Ecalculating the existence probabilities for each angular direction ϕusing the method described above, for example.

Subsequently, the calculation unit 20E converts the polar coordinate mapM1 into an orthogonal coordinate space.

FIG. 13 is a diagram illustrating a relationship between the regions B′in the polar coordinate space and the partitioned regions B in theorthogonal coordinate space. The relationship is illustrated in theorthogonal coordinate space. The regions indicated by the solid lines inFIG. 13 are the partitioned regions B divided into rectangles in theorthogonal coordinate space, and the regions indicated by the brokenlines are the regions obtained by converting the regions B′ divided intorectangles in the polar coordinate space into the orthogonal coordinatespace.

For each partitioned region B in the orthogonal coordinate space, thecalculation unit 20E sets the existence probability of an obstacle inthe closest region B′, among the regions B′ rectangularly divided in thepolar coordinate space, as the existence probability of an obstacle inthe corresponding partitioned region B in the positional relationship asillustrated in FIG. 13. For example, the calculation unit 20E mayperform this setting using a nearest-neighbor method.

By using a bilinear method, furthermore, the calculation unit 20Einterpolates, for each partitioned region B in the orthogonal coordinatespace, the existence probability of an obstacle in the correspondingneighboring region B′ among the regions B′ rectangularly divided in thepolar coordinate space. The existence probabilities of the obstacles inthe partitioned regions B may be set in this way.

Note that these methods are examples of coordinate conversion from thepolar coordinate space into the orthogonal coordinate space, and are notlimited to these methods.

FIG. 14 is a schematic diagram illustrating an example of an orthogonalcoordinate map M2. FIG. 14 is a result obtained by converting the polarcoordinate map M1 illustrated in FIG. 12 into the orthogonal coordinatemap M2 which is in the orthogonal coordinate space.

Note that with respect to the scene in FIG. 7, the existenceprobabilities of obstacles in the regions corresponding to the fences atthe sides of the road on which the mobile object 10 travels are set tothe maximum value or a value close to the maximum value in theorthogonal coordinate space (indicated by “grid-like hatching withdiagonal lines” in FIG. 14). Furthermore, the existence probabilities ofobstacles in the partitioned regions B further back than these regionsare set to the intermediate value (indicated in “gray” in FIG. 14), andthe state is unknown. Furthermore, the partitioned regions B existingbetween the regions where the obstacles exist and the mobile object 10are set to the existence probabilities (indicated in a color between“grid-like hatching with diagonal lines” and “white” and “gray” in FIG.14) that change toward the regions where the obstacles exist from theexistence probabilities of obstacles of the minimum value (indicated in“white” in FIG. 14).

Then, the calculation unit 20E outputs this orthogonal coordinate map M2to the output control unit 20F as existence probability informationindicating the existence probabilities of obstacles.

Note that the calculation unit 20E may integrate the calculatedexistence probability information of obstacles and the existenceprobability information of obstacles calculated in the past in timeseries.

FIG. 15 is an explanatory diagram of the existence probabilitiesintegrated in time series. FIG. 15 illustrates a plurality ofpartitioned regions B obtained by dividing a surrounding space centeredon the mobile object 10 for each of time t-1 and time t.

A region N_(t-1) which is a partitioned region B at the time t-1 and aregion N_(t) which is a partitioned region B at the time t are differentin terms of the relative position from the mobile object 10 at eachtime. However, these of the region N_(t-1) and the region N_(t) which isthe partitioned region B at the time t indicate the same position in aworld coordinate space.

The calculation unit 20E calculates the moving amount of the mobileobject 10 between the time t and the time t-1 from the self position andattitude information. The time t-1 is the time immediately before thetime t. Then, the calculation unit 20E obtains the partitioned regions Bat the time t-1 corresponding to the respective partitioned regions B atthe time t on the basis of the moving amount of the mobile object 10. Inthe example of FIG. 15, the region N_(t-1) is obtained as the region(partitioned region B) at the time t-1 corresponding to the region N_(t)at the time t. Then, the calculation unit 20E integrates the existenceprobability of an obstacle for the region N_(t) (the existenceprobability of the obstacle calculated on the basis of the currentposition information) and the existence probability of an obstaclecalculated in the past for the region N_(t-1). For this integration,Bayes's theorem indicated in the following equation (9) may be used, forexample.

$\begin{matrix}{\frac{p\left( {{m_{i}❘z_{1}},\ldots\mspace{14mu},z_{t}} \right)}{1 - {p\left( {{m_{i}❘z_{1}},\ldots\mspace{14mu},z_{t}} \right)}} = {\frac{p\left( {m_{i}❘z_{t}} \right)}{1 - {p\left( {m_{i}❘z_{t}} \right)}} \cdot \frac{p\left( {{m_{i}❘z_{1}},\ldots\mspace{14mu},z_{t - 1}} \right)}{1 - {p\left( {{m_{i}❘z_{1}},\ldots\mspace{14mu},z_{t - 1}} \right)}}}} & (9)\end{matrix}$

Note that in the equation (9), p(m_(i)|z_(t)) indicates the existenceprobability of an obstacle based on the current position information,p(m_(i)|z₁, . . . , z_(t-1)) indicates the existence probability of anobstacle based on the position information in the past, and p(m_(i)|z₁,. . . , z_(t)) indicates the existence probability of an obstacle basedon the position information up to the present.

By integrating the existence probabilities of obstacles for therespective partitioned regions B in time series, the calculation unit20E can robustly calculate the existence probabilities of obstacles evenin a case where the sensor observes a value including a noise at anytiming, for example.

The description will continue, returning to FIG. 2. The calculation unit20E outputs the calculated existence probability information indicatingthe existence probabilities of obstacles to the output control unit 20F.In the present embodiment, the calculation unit 20E outputs theorthogonal coordinate map M2 to the output control unit 20F. Theorthogonal coordinate map M2 indicates the existence probability of anobstacle for each of the partitioned regions B.

The output control unit 20F outputs the existence probabilityinformation. In the present embodiment, the orthogonal coordinate map M2indicating the existence probability information is output to at leastone of the output unit 10A and the power control unit 10G.

The output control unit 20F displays the existence probabilityinformation on the display 10E. In the present embodiment, the outputcontrol unit 20F displays a display screen including the existenceprobability information on the display 10E.

FIG. 16 is a schematic diagram illustrating an example of a displayscreen 70. The display screen 70 includes the orthogonal coordinate mapM2 calculated by the calculation unit 20E. The orthogonal coordinate mapM2 in the display screen 70 is the same as the orthogonal coordinate mapM2 illustrated in FIG. 14. Note that in the display screen 70, theexistence probabilities of obstacles in the regions corresponding to thefences at the sides of the road on which the mobile object 10 travelsare indicated as regions 70E. Furthermore, the existence probabilitiesof obstacles further back than these regions are indicated as regions70C to which the intermediate value is set. Furthermore, the partitionedregions B existing between regions where the obstacles exist and themobile object 10 are indicated as regions 70A and regions 70B. Theregions 70A are where the existence probabilities of obstacles indicatethe minimum value. The regions 70B indicate the existence probabilitiesthat change from the regions 70A toward the regions where the obstaclesexist. Therefore, the user can easily recognize the existenceprobabilities of obstacles by confirming the display screen 70.

The description will continue, returning to FIG. 2. Furthermore, theoutput control unit 20F may control the display 10E and the speaker 10Fso as to output a sound and light indicating the existence probabilityinformation. Furthermore, the output control unit 20F may transmit theexistence probability information to the external apparatus via thecommunication unit 10D.

Furthermore, the output control unit 20F may output the existenceprobability information to the power control unit 10G.

In this case, the power control unit 10G controls the power unit 10Haccording to the existence probability information received from theoutput control unit 20F. For example, the power control unit 10G maygenerate a power control signal for controlling the power unit 10Haccording to the existence probability information and control the powerunit 10H. The power control signal is a control signal for controllingthe drive unit that drives the traveling of the mobile object 10 in thepower unit 10H. For example, the power control unit 10G controlssteering, engine, and the like, of the mobile object 10 such that themobile object 10 travels in regions of a real space corresponding to thepartitioned regions B whose existence probabilities are indicated astravelable regions by the existence probability information.

Subsequently, an example of a procedure of information processingexecuted by the information processing apparatus 20 will be described.FIG. 17 is a flowchart illustrating the example of the procedure of theinformation processing.

First, the position acquisition unit 20C acquires position-relatedinformation (step S100). Subsequently, the shape acquisition unit 20Dacquires ground surface shape information 36 indicating the shape of theground surface (step S102).

Subsequently, the calculation unit 20E calculates the existenceprobabilities of obstacles according to the first distances and thesecond distances of detection points P on the basis of theposition-related information acquired in step S100 and the groundsurface shape information 36 acquired in step S102 (step S104).

Subsequently, the output control unit 20F outputs existence probabilityinformation calculated in step S104 (step S106). In the presentembodiment, the orthogonal coordinate map M2 indicating the existenceprobability information is output to at least one of the output unit 10Aand the power control unit 10G.

Subsequently, the processing unit 20A determines whether to end theprocessing (step S108). When the determination in step S108 is negative(step S108: No), the processing returns to step S100. On the other hand,when the determination in step S108 is positive (step S108: Yes), thisroutine ends.

As described above, the information processing apparatus 20 according tothe present embodiment includes the position acquisition unit 20C, theshape acquisition unit 20D, and the calculation unit 20E. The positionacquisition unit 20C acquires three-dimensional position information ofdetection points P. The shape acquisition unit 20D acquires targetsurface shape information (ground surface shape information 36)indicating the shape of the target surface (ground surface). Thecalculation unit 20E calculates state information of objects (existenceprobabilities of obstacles) according to the first distances of thedetection points P from the ground surface on the basis of thethree-dimensional position information and the target surface shapeinformation (ground surface shape information 36).

In this way, the information processing apparatus 20 according to thepresent embodiment calculates the existence probabilities of obstacles(state information of objects) according to the distances of thedetection points P from the ground surface (target surface).Accordingly, it is possible to suppress the occurrence that a travelableregion such as a ground surface is erroneously recognized as anobstacle. More specifically, even in a case where a position indicatedby the three-dimensional position information of a detection point Pindicates a target surface such as a ground surface, it is possible tosuppress the occurrence that the region including the detection point Pis recognized as an object such as an obstacle.

Therefore, the information processing apparatus 20 according to thepresent embodiment can improve the reliability of object detection.

Furthermore, the information processing apparatus 20 according to thepresent embodiment derives the first distances of the detection points Pfrom the ground surface on the basis of the ground surface shapeinformation 36. Therefore, it is possible to suppress the influence ofestimation errors of the ground surface shape, thereby improving thereliability of the object detection.

First Modification

Note that in the above embodiment, the position acquisition unit 20Cacquires the three-dimensional position information of detection pointsP from the external sensor 10B, and the shape acquisition unit 20Dgenerates the ground surface shape information 36 using thisthree-dimensional position information.

However, the position acquisition unit 20C and the shape acquisitionunit 20D may acquire the three-dimensional position information of thedetection points P using different external sensors 10B.

In this case, a first external sensor 10J and a second external sensor10K may be included as the external sensors 10B as illustrated in FIG.2. The first external sensor 10J and the second external sensor 10K aresimilar to the external sensor 10B described above. Note that the firstexternal sensor 10J and the second external sensor 10K may be physicallydifferent sensors. Specifically, the first external sensor 10J and thesecond external sensor 10K just need to be mutually different types ofthe external sensors 10B.

For example, a photographing apparatus (a monocular camera or a stereocamera) is used as the first external sensor 10J, and a laser imagingdetection and ranging (LIDAR) sensor such as a two-dimensional LIDARsensor or a three-dimensional LIDAR sensor mounted parallel to ahorizontal plane is used as the second external sensor 10K.

Then, the position acquisition unit 20C acquires the three-dimensionalposition information of detection points P from the second externalsensor 10K such as the LIDAR sensor and outputs the three-dimensionalposition information to the calculation unit 20E. Furthermore, the shapeacquisition unit 20D may acquire the three-dimensional positioninformation of the detection points P from the first external sensor 10Jsuch as a camera and generate ground surface shape information 36. Then,the shape acquisition unit 20D may output the ground surface shapeinformation 36 generated using the three-dimensional positioninformation of the detection points P acquired from the first externalsensor 10J to the calculation unit 20E. The processing of thecalculation unit 20E and the output control unit 20F are similar tothose in the above embodiment.

Note that the shape acquisition unit 20D may acquire thethree-dimensional position information of the detection points P fromthe second external sensor 10K such as the LIDAR sensor and generate theground surface shape information 36. Then, the position acquisition unit20C may acquire the three-dimensional position information of thedetection points P from the first external sensor 10J such as a cameraand output the three-dimensional position information to the calculationunit 20E.

Note that in a case where second external sensor 10K is thetwo-dimensional LIDAR, the optical axis of the LIDAR may be horizontalto the traveling direction as illustrated in FIG. 11A-1. Furthermore,the optical axis of the LIDAR may be inclined to the traveling directionas illustrated in FIG. 11B-1. Furthermore, even in a case where thesecond external sensor 10K is the three-dimensional LIDAR capable ofmeasuring the distance information of a plurality of lines, theinformation processing apparatus 20 can also recognize a surroundingenvironment on the basis of the existence probabilities of obstaclescalculated for the distance information of each line.

In the present modification, the position acquisition unit 20C and theshape acquisition unit 20D acquire the three-dimensional positioninformation of the detection points P using the different externalsensors 10B in this way. In the present modification, therefore, evenfor sensor information that is difficult to estimate the ground surfaceshape with a sensor alone such as one-line LIDAR, the informationprocessing apparatus 20 can calculate the existence probabilities ofobstacles by using the ground surface shape information estimated fromanother sensor. In the present modification, furthermore, theinformation processing apparatus 20 can suppress erroneous detection ofpart of the ground surface such as a road as an obstacle, and suppressundetected obstacles.

Second Modification

Note that in the above embodiment, the position acquisition unit 20Cacquires the three-dimensional position information of the detectionpoints P from the external sensor 10B mounted in the mobile object 10.Furthermore, the shape acquisition unit 20D acquires the ground surfaceshape information 36 on the basis of the three-dimensional positioninformation acquired from the external sensor 10B mounted in the mobileobject 10.

However, at least one of the position acquisition unit 20C and the shapeacquisition unit 20D may acquire the three-dimensional positioninformation of the detection points P from an external apparatus. Inthis case, at least one of the position acquisition unit 20C and theshape acquisition unit 20D may acquire the three-dimensional positioninformation of the detection points P from the external apparatus viathe communication unit 10D.

Furthermore, the shape acquisition unit 20D may acquire the groundsurface shape information 36 by receiving the ground surface shapeinformation 36 from the external apparatus via the communication unit10D.

Next, an example of a hardware configuration of the informationprocessing apparatus 20 according to the above embodiment will bedescribed. FIG. 18 is the example of the hardware configuration of theinformation processing apparatus 20 according to the above embodiment.

The information processing apparatus 20 according to the aboveembodiment includes a control apparatus, storage apparatuses, an I/Funit 82, an output unit 80, an input unit 94, and a bus 96, and has ahardware configuration using a typical computer. The control apparatusis a CPU 86 or the like. The storage apparatuses are a read only memory(ROM) 88, a random access memory (RAM) 90, a hard disk drive (HDD) 92,and the like. The I/F unit 82 serves as an interface with variousdevices. The output unit 80 outputs various kinds of information such asoutput information. The input unit 94 receives user operations. The bus96 connects above units.

In the information processing apparatus 20 according to the aboveembodiment, the CPU 86 reads a program from the ROM 88 onto the RAM 90and executes the program so that the above units are performed on thecomputer.

Note that the program for executing the above processing to be executedby the information processing apparatus 20 according to the aboveembodiment may be stored in the HDD 92. Furthermore, the program forexecuting the above processing to be executed by the informationprocessing apparatus 20 according to the above embodiment may beincorporated preliminarily and provided in the ROM 88.

Furthermore, the program for executing the above processing to beexecuted by the information processing apparatus 20 according to theabove embodiment may be stored and provided in the form of aninstallable or executable file as a computer program product in acomputer-readable storage medium such as a compact disc read only memory(CD-ROM), a compact disc recordable (CD-R), a memory card, a digitalversatile disk (DVD), or a flexible disk (FD). Furthermore, the programfor executing the above processing to be executed by the informationprocessing apparatus 20 according to the above embodiment may be storedon a computer connected to a network such as the Internet, and providedby being downloaded via the network. Furthermore, the program forexecuting the above processing to be executed by the informationprocessing apparatus 20 according to the above embodiment may beprovided or distributed via a network such as the Internet.

While certain embodiments have been described, these embodiments havebeen presented by way of example only, and are not intended to limit thescope of the inventions. Indeed, the novel embodiments described hereinmay be embodied in a variety of other forms; furthermore, variousomissions, substitutions and changes in the form of the embodimentsdescribed herein may be made without departing from the spirit of theinventions. The accompanying claims and their equivalents are intendedto cover such forms or modifications as would fall within the scope andspirit of the inventions.

What is claimed is:
 1. An information processing apparatus comprising: amemory; processing circuitry configured to acquire three-dimensionalposition information of a plurality of detection points; acquire targetsurface shape information indicating a shape of a target surface; andcalculate state information of an object according to one respectivefirst distance of a respective one of the plurality of detection pointsfrom the target surface on the basis of the three-dimensional positioninformation and the target surface shape information, wherein thecalculating calculates the state information which is lower when thefirst distance is shorter, and wherein the processing circuitry isconfigured to calculate the state information which is lower asrespective of second distances between positions of respective of onesof the plurality of detection points indicated by the three-dimensionalposition information and a reference position is longer.
 2. Theinformation processing apparatus according to claim 1, wherein the stateinformation indicates an probability that the object exists.
 3. Theinformation processing apparatus according to claim 1, wherein in a casewhere the first distance is 0 or less, the processing circuitry isconfigured to calculate an intermediate value indicating that a state isunknown as the state information.
 4. The information processingapparatus according to claim 1, wherein the processing circuitry isconfigured to calculate the state information according to therespective first distance of the detection point among the plurality ofdetection points closest to a reference position for an angulardirection from the reference position in a polar coordinate space. 5.The information processing apparatus according to claim 4, wherein theprocessing circuitry is configured to calculate: the state informationof the plurality of detection points according to the respective firstdistance of the detection point among the plurality of detection pointsclosest to the reference position for the angular direction from thereference position in the polar coordinate space; and a model indicatinga change according to a distance from the reference position and thestate information of the closest detection point along a line extendingin the angular direction from the reference position in the polarcoordinate space.
 6. The information processing apparatus according toclaim 1, wherein the processing circuitry is configured to acquire thetarget surface shape information according to distribution of theplurality of the detection points.
 7. The information processingapparatus according to claim 1, wherein the processing circuitry isconfigured to acquire the three-dimensional position information of theplurality of detection points from a first external sensor, and acquirethe target surface shape information by generating the target surfaceshape information according to the three-dimensional positioninformation of the plurality of detection points detected by a secondexternal sensor.
 8. The information processing apparatus according toclaim 7, wherein the processing circuitry is configured to acquire thethree-dimensional position information from an external apparatus. 9.The information processing apparatus according to claim 1, wherein theprocessing circuitry is configured to acquire output the stateinformation.
 10. An information processing method comprising: acquiringthree-dimensional position information of a plurality of detectionpoints; acquiring target surface shape information indicating a shape ofa target surface; and calculating state information of an objectaccording to one respective first distance of a respective one of theplurality of detection points from the target surface on the basis ofthe three-dimensional position information and the target surface shapeinformation, wherein the calculating calculates the state informationwhich is lower when the first distance is shorter, and wherein, in thecalculating, lower state information is calculated as respective ofsecond distances between positions of respective of ones of theplurality of detection points indicated by the three-dimensionalposition information and a reference position is longer.
 11. Theinformation processing method according to claim 10, wherein the stateinformation indicates an existence probability of the object.
 12. Theinformation processing method according to claim 10, wherein, in thecalculating, lower state information is calculated as the first distanceis shorter.
 13. The information processing method according to claim 10,wherein, in the calculating, in a case where the first distance is 0 orless, an intermediate value is calculated as the state information. 14.The information processing method according to claim 10, wherein, in thecalculating, the state information is calculated according to therespective first distance of the detection point among the plurality ofdetection points closest to a reference position for an angulardirection from the reference position in a polar coordinate space. 15.The information processing method according to claim 14, wherein, in thecalculating, the state information of the detection point is calculatedaccording to the respective first distance of the detection point amongthe plurality of detection points closest to the reference position forthe angular direction from the reference position in the polarcoordinate space; and a model is calculated, the model indicating achange according to a distance from the reference position and the stateinformation of the closest detection point along a line extending in theangular direction from the reference position in the polar coordinatespace.
 16. The information processing method according to claim 10,wherein, in acquiring of the target surface shape information, thetarget surface shape information according to distribution of theplurality of the detection points is acquired.
 17. The informationprocessing apparatus according to claim 1, wherein the processingcircuitry is further configured to display, on a display unit, a displayscreen including the state information of the object.